Categorized social opinions as answers to questions

ABSTRACT

A question is analyzed to determine a set of categories, a category corresponding to a type of possible answers responsive to the question. A set of opinions is extracted from social media data. Each opinion is from a corresponding responder on a social media platform to which the question is sent. An opinion from the set of opinions is categorized into a category from the set of categories. A strength of the opinion is computed using a subset of a set of strength parameters. The opinion is ranked according to the strength of the opinion. The opinion, the strength, and the category are presented in a filterable presentation.

TECHNICAL FIELD

The present invention relates generally to a method, system, andcomputer program product for finding answers to a user's query. Moreparticularly, the present invention relates to a method, system, andcomputer program product for categorizing social opinions as answers toquestions.

BACKGROUND

Presently, in order to find information, a user submits a search stringto a search engine. The search engine selects from the information thatthe engine has indexed that information which is fully or partiallyrelated to the search string. Such selected information becomes thesearch engine's result set. The search engine uses a search algorithm tosearch the index, an ordering algorithm to arrange the result set insome order—generally an order of relevance to the question, age of theinformation, or both.

A user, who is a member of a social media platform can also ask aquestion in the user's social network. Asking a question to the socialnetwork is another way of finding information. A member of the socialnetwork responds to the question with an answer. The answer is generallyan opinion of the responding member.

Hereinafter, a user can be a human, a system, or an application, unlessexpressly distinguished where used. Any reference to a query or a searchquery is a reference to a string of letter, words, or phrases in anatural language, which can be used to search a repository ofinformation. The query need not be in any particular query language. Aquestion is an expression in any suitable form of a desire to obtaininformation. An asker is a user who asks a question. A social mediaparticipant who responds to an asker's question is a responder or ananswerer. The responder may be, but need not be, a member of the asker'ssocial network. An answer provided by a responder is an opinion of theresponder.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product. An embodiment includes a method that analyzes aquestion to determine a set of categories, a category corresponding to atype of possible answers responsive to the question. The embodimentextracts from social media data a set of opinions, wherein each opinionis from a corresponding responder on a social media platform to whichthe question is sent. The embodiment categorizes an opinion from the setof opinions into a category from the set of categories. The embodimentcomputes a strength of the opinion using a subset of a set of strengthparameters. The embodiment ranks the opinion according to the strengthof the opinion. The embodiment presents the opinion, the strength, andthe category in a filterable presentation.

An embodiment includes a computer usable program product. The computerusable program product includes one or more computer-readable storagedevices, and program instructions stored on at least one of the one ormore storage devices.

An embodiment includes a computer system. The computer system includesone or more processors, one or more computer-readable memories, and oneor more computer-readable storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example configuration forcategorizing social opinions as answers to questions in accordance withan illustrative embodiment;

FIG. 4 depicts a block diagram of an application for categorizing socialopinions as answers to questions in accordance with an illustrativeembodiment;

FIG. 5A depicts a block diagram of an example manner of constructing andusing a responder-specific language model in accordance with anillustrative embodiment;

FIG. 5B depicts a block diagram of an example manner of constructing andusing a responder-specific language model in accordance with anillustrative embodiment; and

FIG. 6 depicts a flowchart of an example process for categorizing socialopinions as answers to questions in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize that while there are some ways toorganize and prioritize the search result set from a traditionalinternet search engine, opinions on social media are only presented tothe asker as the opinions are posted. The illustrative embodimentsrecognize that presently, there does not exist a mechanism by which theopinions can be sought, collected, analyzed, organized, ranked accordingto their strengths, and presented in a drill-down or filterable mannerto the asker.

For example, the illustrative embodiments recognize that differentresponders can have differing opinions on the same question. Presently,the asker is simply presented with the various opinions, and the askerhas to review each one and determine the merits of each opinion. Whennumerous opinions are presented to the asker, a distinct risk existsthat the asker may not read some of the opinions which could bevaluable, or may interpret the opinion incorrectly, or may fail toattribute a correct weight or strength to the opinion in view of thecurrent affairs, the state of other information existing on the internetand the relationship of the opinion with such information, and the like.

For example, an asker might want to weigh those opinions higher thanothers, which are supported by the responder's personal experience orother evidence. Some examples of such evidentiary support include butare not limited to a responder's own experience, an experience of amember of a social network of the responder, factual informationavailable through traditional search, and the like. When manuallyscanning the opinions, an asker's ability is limited not only by thehuman effort involved, but also the unavailability or unawareness ofsuch evidence to the human. A method for automatic analysis of opinions,classification into categories that are related to the question,computation of the strength or weight of the opinion according to anumber of parameters, ranking of the opinions, and presenting in afilterable manner to the asker would therefore be useful.

The illustrative embodiments recognize that the presently availabletools or solutions do not address these needs/problems or provideadequate solutions for these needs/problems. The illustrativeembodiments used to describe the invention generally address and solvethe above-described problems and other related problems by categorizingsocial opinions as answers to questions.

An embodiment can be implemented as a software application. Theapplication implementing an embodiment, or one or more componentsthereof, can be configured as a modification of an existing social mediaplatform—i.e., a native application in the social media platform, as anapplication executing in a data processing system communicating with anexisting social media platform over a local area network (LAN)—i.e., alocal application on the LAN, as an application executing in a dataprocessing system communicating with an existing called system over awide area network (WAN)—i.e., a remote application on the WAN, as aseparate application that operates in conjunction with the existingsocial media platform in other ways, a standalone application, or somecombination thereof.

An embodiment determines that an asker is expressing a need forinformation from social media. The embodiment determines the types ofanswers that are possible for the type of question the asker is asking.For example, some questions can be answered by affirmative or negativeanswer and various degrees thereof. As another example, some otherquestions can be answered by a variety of option. As another example,some other questions can be answered by anecdotal answers, factualanswers, hypothetical answers, and experimental answers.

These examples of types of answers are not intended to be limiting. Fromthis disclosure, those of ordinary skill in the art will be able toconceive many other answer types, and the same are contemplated withinthe scope of the illustrative embodiments.

Furthermore, in a particular embodiment, the types of answers that aresuitable for a question can be determined by analyzing the question,e.g., using Natural Language Processing (NLP) to identify the topic andother characteristics of the question. Analyzing historical informationabout how users generally answer questions on that topic, or questionshaving those characteristics, is one example non-limiting manner ofdetermining the types of answers that can be expected for the question.Such historical information can be obtained by data mining a socialmedia data source, information available on the internet, or both.

Based on the identified types of answers that are possible for the typeof question, the embodiment constructs a set of categories thatcorrespond to the question. A category corresponds to a type of answers.

One embodiment further formats, arranges, composes, or otherwisereorganizes the question, if needed, to present to a social mediaplatform for opinions. Another embodiment further formats, arranges,composes, or otherwise reorganizes the question, if needed, to presentto a traditional search engine for obtaining a result set.

In response to the question, or a variation thereof, a social media datasource returns social data that includes a set of opinions. Anembodiment extracts the set of opinions from the social data. Theembodiment analyzes an opinion to determine a category of the opinion,from the previously determined set of categories corresponding to thequestion. The embodiment classifies or categorizes the opinion into thedetermined category, and repeats the categorization process for otheropinions in the set of opinions.

For the subset of opinions classified into a category, an embodimentcomputes a strength of the opinion. A strength of an answer, e.g., anopinion, is dependent upon one or more strength parameters. A strengthparameter is a value computed using a particular aspect of the answer.Some non-limiting examples of strength parameters are as follows

A personal language model—different responders use different manners ofexpressing their opinions. One responder may always show excitement inexpressing an opinion, whereas another responder may always be cautiousin expressing an opinion, regardless of how exciting or mundane thesubject of those opinions might be. The value or strength of eachopinion has to be scaled up or down depending upon the manner in which aparticular responder uses the natural language. Accordingly, anembodiment obtains from the social data source historical data of thesocial media contributions by a responder. The embodiment analyzes thehistorical data of the responder to construct a responder-specificlanguage model. The personal language model includes a set of entries,where an entry depicts a correspondence between a word or phrase used inan expression by the responder and an actual meaning or intent of theexpression. The embodiment measures the language of the opinion usingthe responder-specific language model, to compute a language style-basedstrength parameter of the opinion.

A cultural model—different responders from different culturalbackgrounds may express similar opinions differently (and differentopinions in manners that may make them appear similar to one another).One responder may always be upfront and open about everything, whereasanother responder may always be courteous and polite in expressing anopinion. The value or strength of each opinion has to be scaled up ordown depending upon the cultural background of a particular responder.Accordingly, an embodiment obtains from the social data sourcehistorical data of the social media contributions by a responder. Theembodiment also obtains from the social media information that isindicative of the responder's cultural background. The embodimentanalyzes the historical data and the cultural information of theresponder to construct a responder-specific cultural model. The culturalmodel includes a set of entries, where an entry depicts a correspondencebetween a culturally-influenced expression of the responder and anactual meaning or intent of the expression. The embodiment determines acorrect meaning of the opinion using the responder-specific culturalmodel, to compute a culture-based strength parameter of the opinion.

Personal experience—different responders may have different personalexperiences to support their opinion. The personal experience is oneexample form of evidentiary support for the opinion. One responder mayactually have some personal experience with the subject of theresponder's opinion, whereas another responder may be opininghypothetically and without any personal experience. Note that personalexperience can be an experience of the responder or of another userwhere the other user and the other user's experience are known to theresponder. The value or strength of each opinion is scaled up or downdepending upon the personal experience of a particular responder.Accordingly, an embodiment obtains from the social data sourcehistorical data of the social media contributions by a responder. Theembodiment analyzes the historical data of the responder to establishwhether the responder or the responder's social network has anyexperience with the subject of the opinion. The embodiment computes anexperience-based strength parameter of the opinion based on thisanalysis.

Other evidentiary support—in a similar manner, an embodiment determineswhether some evidence exists to support a responder's opinion. In oneexample manner, the embodiment sends to a traditional search engine asearch query corresponding to the question, and obtains a result set.The embodiment analyzes the search results in the result set todetermine whether any information source has provided information thatsupports the opinion of the responder. The embodiment uses the analysisto compute a degree of evidentiary support that exists in the resultset. The degree of evidentiary support depends on a number of resultsthat support the opinion, a level at which the support is provided(e.g., completely supportive, marginally supporting, non-supporting,cautiously supportive, tentatively supportive, etc., and other levels ofsupports), a provenance of the information source that is providing thesupport, or a combination thereof. The embodiment computes anevidence-based strength parameter of the opinion based on this analysis.

Social media support—different opinions of different responders may beshared, liked, disliked, or commented upon by other social media users.This manner of social media interaction with an opinion is referred toherein as social media support for the opinion. The social media supportis another example form of evidentiary support for the opinion. Oneopinion may be better supported in social media as compared to anotheropinion. The value or strength of each opinion has to be scaled up ordown depending upon the personal experience of a particular responder.Accordingly, an embodiment obtains from the social data source socialmedia support data corresponding to the opinion of a responder. Theembodiment analyzes the social media support data to computes socialmedia support-based strength parameter of the opinion.

These examples of manners of strength parameters and strengthcomputations are not intended to be limiting. From this disclosure,those of ordinary skill in the art will be able to conceive many otherstrength parameters and strength computations and the same arecontemplated within the scope of the illustrative embodiments.

Using the values computed for one or more strength parameters in thismanner, an embodiment computes an overall strength value of the opinion.This overall strength value of the opinion is usable in ranking theopinion within the set of opinions. One embodiment computes the rank ofan opinion across the subset of opinions that are within the category ofthe opinion. Another embodiment computes the ranking of the opinionacross all opinions in the set of opinions regardless of the categories.Another embodiment also computes a strength of a category based on thestrength of the constituent subset of opinions.

An embodiment presents the categorized and strength-ranked opinions tothe asker. In one example, the highest-ranking opinion across allopinions in the set of opinions (and the opinion's category) may belisted first, followed by other opinions and their categories inprogressively decreasing strength ranking. In another example, thecategory with the highest category strength may be listed first,followed by other categories in progressively decreasing categorystrengths. Within the categories listed in this manner, an embodimentlists the opinions of that category according to the opinions' strengthrankings.

These example manners of presenting the opinions to the asker provide amechanism for the asker to drill-down into the presentation of theopinions according to a variety of criteria, including but not limitedto drill-down by category, category strength, opinion strength, andstrength parameters used in the computation of opinion strengths. Forexample, the asker can filter (or drill-down) the presentation to showonly those opinions that have personal experience based strength valuesin a specified range, or show only those opinions that have a certaincultural influence, or show only those opinions that have evidentiarysupport from an information source of a certain provenance, and so on.

The manner of categorizing social opinions as answers to questionsdescribed herein is unavailable in the presently available methods. Amethod of an embodiment described herein, when implemented to execute ona device or data processing system, comprises substantial advancement ofthe functionality of that device or data processing system in organizingthe social opinions in response to questions in an effective,categorized, ranked, and filterable manner.

The illustrative embodiments are described with respect to certain typesof questions, answers, opinions, strength parameters, computations,analyses, ranks, categories, drill-down or filtering, presentation,devices, data processing systems, environments, components, andapplications only as examples. Any specific manifestations of these andother similar artifacts are not intended to be limiting to theinvention. Any suitable manifestation of these and other similarartifacts can be selected within the scope of the illustrativeembodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas example and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner.

Application 105 implements an embodiment described herein. Application105 receives a question from a user of device 132, e.g., via socialmedia interface 134 in device 132. Application 105 obtains social datafrom social media data source 107. Application 105 extracts,categorizes, and ranks the opinions from the social data. Application105 presents the categorized and ranked opinions on social mediainterface 134. The user can drill-down or filter the presentation of theopinions using social media interface 134 or another application ondevice 132.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114,and device 132 may couple to network 102 using wired connections,wireless communication protocols, or other suitable data connectivity.Clients 110, 112, and 114 may be, for example, personal computers ornetwork computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Data processing environment 100 may also take the form of a cloud, andemploy a cloud computing model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g. networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 in FIG. 1,are located on storage devices, such as in the form of code 226A on harddisk drive 226, and may be loaded into at least one of one or morememories, such as main memory 208, for execution by processing unit 206.The processes of the illustrative embodiments may be performed byprocessing unit 206 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 208, read onlymemory 224, or in one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. in another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and disk 226 is manifested as a virtualizedinstance of all or some portion of disk 226 that may be available in thehost data processing system. The host data processing system in suchcases is represented by data processing system 200.

With reference to FIG. 3, this figure depicts a block diagram of anexample configuration for categorizing social opinions as answers toquestions in accordance with an illustrative embodiment. Application 302is an example of application 105 in FIG. 1. Social media interface 304is an example of social media interface 134 in FIG. 1. Social media datasource 306 is an example of social media data source 107 in FIG. 1.

Asker 308 provides input 310 to interface 304. Input 310 is any suitablemanner of expressing an interest in obtaining information, i.e., aquestion. Interface 304 sends question 312 to application 302. Question312 may be the same as input 310 or a modified form of input 310,depending upon the implementation.

Application 302 presents question 314 to social media platform 316.Question 314 may be the same as question 312 or a modified form ofquestion 312, depending upon the implementation. Presenting question 314causes social media platform 316 to collect opinions 318 from respondersin social network 320. Source 306 send social data 322, which includesopinions 318 and other information as described herein.

In order to collect evidence of support for an opinion, application 302also constructs query 324 and sends to traditional search engine 326.Query 324 may be the same as question 312 or a modified form of question312, depending on the implementation. Search engine 326 returns resultset 328 to application 302.

Application 302 classifies the opinions from social data 322 into one ormore categories suitable for question 312. Application 302 computes astrength of an opinion using portions of social data 322, result set328, or both, as the case may be. Application 302 computes one or moreof a category strength, a within-category ranking of opinions, anoverall ranking of opinions, and a ranking based on a specific type ofstrength parameter.

Application 302 returns categorized and ranked options 330 to interface304 for presenting to asker 308. Asker 308 can drill-down or filteroptions 330 in a manner described herein.

With reference to FIG. 4, this figure depicts a block diagram of anapplication for categorizing social opinions as answers to questions inaccordance with an illustrative embodiment. Application 402 can be usedas application 302 in FIG. 3.

Application 402 receives input 404, which is a question similar toquestion 312 in FIG. 3. Application 402 receives input 406, which issocial data similar to social data 322 in FIG. 3. Application 402receives input 408, which is a result set similar to result set 328 inFIG. 3.

Component 410 uses input question 404 to construct a social searchquery, e.g., question 411A similar to question 314 in FIG. 3, query 411Bsimilar to query 324 in FIG. 3, or both. Social data 406 is responsiveto question 411A. Result set 408 is responsive to query 411B.

Component 410 also constructs a set of categories corresponding to inputquestion 404. For example, subcomponent 412 uses an NLP engine todetermine a topic of input question 404.

Component 414 identifies a set of opinions included in social data input406, and extracts the set from input 406. Component 416 categorizes theopinions in a manner described herein using the categories constructedby component 410. Machine learning can be used to train opinionclassifier component 416 using the opinions presented to component 416,e.g., to learn the opinion intent in phrases and sentences and toclassify opinion statements with various levels of the opinionstrengths.

Component 418 computes one or more strength parameters to compute astrength of an opinion. As one non-limiting example, subcomponent 420computes a strength parameter by building a personal language model fora responder, and evaluating the responder's opinion relative to theresponder-specific language model.

As another non-limiting example, subcomponent 422 computes a strengthparameter by building a personal cultural model for a responder, andevaluating the responder's opinion relative to the responder-specificcultural model. As another non-limiting example, subcomponent 424computes a strength parameter by analyzing direct or indirect supportfor a responder's opinion in social data 406, e.g., in the form ofpersonal experience of the responder or a user known to the responder.Subcomponent 424 evaluates the responder's opinion based on suchsupport.

As another non-limiting example, subcomponent 426 computes a strengthparameter by analyzing social media support for a responder's opinion insocial data 406, e.g., in the form of shares, likes, comments relativeto the responder's opinion. Subcomponent 426 evaluates the responder'sopinion based on such social media support. As another non-limitingexample, subcomponent 428 computes a strength parameter by analyzingresult set 408 to find a degree of support for a responder's opinion.Subcomponent 428 evaluates the responder's opinion based on suchsupport.

Component 430 ranks the opinions in one or more manners describedherein. Component 432 constructs presentation 433 of ranked andcategorized opinions. Subcomponent 434 provides one or more methods ofdrilling down in presentation 433 in a manner described herein.

It is possible that for some questions, competing opinions might appearwith substantially equal strengths. In some cases, different categoriesof opinions having substantially equal strengths, or different opinionshaving comparable strengths might be acceptable to the user. In othercases, resolving a clear winner, i.e., finding a singular category withthe highest strength, or a singular opinion with the highest strengthmight be desirable.

In such cases, where a tie between categories or opinions has to bebroken, an embodiment can be adapted to add a loop in the questionanswer method whereby a question can be modified based on the answers inthe first iterations. The modified embodiment can be configured topresent a second question as a follow-up. In one adaptation, theembodiment constructs the follow-up question automatically based on thecompeting opinions or categories of opinions received in the previousiteration. In another adaptation, the embodiment allows the user toconstruct the follow-up question upon the presentment of the competingopinion or categories.

For example, the user reviews the categorized and ranked opinions. Theuser determines that at least two opinions or categories are rankedsubstantially equally, or another reason exists in the categorized andranked opinions to ask a follow-up question. The user composes andpresents a follow-up question in the manner of question 404.

With reference to FIG. 5A, this figure depicts a block diagram of anexample manner of constructing and using a responder-specific languagemodel in accordance with an illustrative embodiment. Component 502implements the functionality in the manner of subcomponent 420 of FIG.4.

Responder-specific language model 504A is constructed for responder X.Assume that responder X has expressed an opinion “I am appalled” inresponse to a question from an asker. Component 502 receives input 506A,which is the historical social data of responder X. Component 502 alsoreceives opinion 508A, which is the example opinion of responder Xdescribed above.

Using social data 506A, model 504A constructs a language modelvocabulary specific to responder X. In the depicted example, component502 has determined that responder X is prone to using mild words orphrases of expression, such as “concerned”, “sketchy”, “not cool”,“agree”, etc. Accordingly, model 504A includes such words or somelinguistic expression thereof, as shown.

Measuring opinion 508A against model 504A, component 502 finds thatopinion 508A is more strongly expressed than what model 504A suggestsfor responder X. Accordingly, component 502 scales up the strength ofopinion 508A, e.g., to strength 510A, which is depicted as anon-limiting example value of 9 on an arbitrary scale of 1-10.

With reference to FIG. 5B, this figure depicts a block diagram of anexample manner of constructing and using a responder-specific languagemodel in accordance with an illustrative embodiment. Component 502implements the functionality in the manner of subcomponent 420 of FIG.4.

Responder-specific language model 504B is constructed for responder X.Assume that responder X has expressed an opinion “I am appalled” inresponse to a question from an asker. Component 502 receives input 506B,which is the historical social data of responder Y. Component 502 alsoreceives opinion 508B, which is the example opinion of responder Ydescribed above.

Using social data 506B, model 504B constructs a language modelvocabulary specific to responder Y. In the depicted example, component502 has determined that responder Y is prone to using superlative wordsor phrases of expression, such as terrible, disgusting, completelyunhappy, etc. Accordingly, model 504B includes such words or somelinguistic expression thereof, as shown.

Measuring opinion 508B against model 504B, component 502 finds thatopinion 508B is expressed approximately with the same vigor as whatmodel 504B suggests for responder Y. Accordingly, component 502 scalesdown, or does not adjust, the strength of opinion 508B, e.g., tostrength 510B, which is depicted as a non-limiting example value of 5 onan arbitrary scale of 1-10.

With reference to FIG. 6, this figure depicts a flowchart of an exampleprocess for categorizing social opinions as answers to questions inaccordance with an illustrative embodiment. Process 600 can beimplemented in application 402 in FIG. 4.

The application receives from an asker a question for a social mediaplatform (block 602). The application optionally sends the question or aversion thereof to the social media platform if the question has notalready been send to the platform (block 604).

The application analyzes the question to identify a topic of thequestion (block 606). The application determines a set of categories ofanswers that are possible for the topic in question (block 607).

The application receives social data containing a set of opinions inresponse to the question (block 608). The application extracts the setof opinions from the social data (block 610).

The application classifies an opinion into one or more categories fromblock 607 (block 612). The application repeats block 612 for any numberof opinions.

The application computes a strength of each opinion in a category, basedon one or more strength parameter computations, as described herein(block 614). The application repeats block 614 for any number ofstrength computations.

The application ranks the categories according to category-strength,relevance to the question, expectation of the asker or the question, orsome combination of these and other considerations (block 616). Theapplication ranks the opinions in a category, which itself may be rankedamong the set of categories according to block 616 (block 618). Theapplication repeats block 618 for any number categories and the opinionstherein.

The application presents the ranked and categorized opinions to theasker (block 620). The application may end process 600 thereafter, ormay optionally provide additional functionality as described in blocks622-626.

For example, the application may receive a drill-down or filtering inputfrom the asker upon presentation from block 620 (block 622). Theapplication re-arranges the opinions and/or the categories according toinput (block 624). The application presents the drilled-down or filteredview of the opinions (block 626). The application ends thereafter.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forcategorizing social opinions as answers to questions and other relatedfeatures, functions, or operations. Where an embodiment or a portionthereof is described with respect to a type of device, the computerimplemented method, system or apparatus, the computer program product,or a portion thereof, are adapted or configured for use with a suitableand comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

1-16. (canceled)
 17. A computer usable program product comprising one ormore computer-readable storage devices, and program instructions storedon at least one of the one or more storage devices, the stored programinstructions comprising: program instructions to analyze a question todetermine a set of categories, a category corresponding to a type ofpossible answers responsive to the question; program instructions toextract from social media data a set of opinions, wherein each opinionis from a corresponding responder on a social media platform to whichthe question is sent; program instructions to categorize an opinion fromthe set of opinions into a category from the set of categories; programinstructions to compute a strength of the opinion using a subset of aset of strength parameters; program instructions to rank the opinionaccording to the strength of the opinion; and program instructions topresent the opinion, the strength, and the category in a filterablepresentation.
 18. The computer usable program product of claim 17,wherein the computer usable code is stored in a computer readablestorage device in a data processing system, and wherein the computerusable code is transferred over a network from a remote data processingsystem.
 19. The computer usable program product of claim 17, wherein thecomputer usable code is stored in a computer readable storage device ina server data processing system, and wherein the computer usable code isdownloaded over a network to a remote data processing system for use ina computer readable storage device associated with the remote dataprocessing system.
 20. A computer system comprising one or moreprocessors, one or more computer-readable memories, and one or morecomputer-readable storage devices, and program instructions stored on atleast one of the one or more storage devices for execution by at leastone of the one or more processors via at least one of the one or morememories, the stored program instructions comprising: programinstructions to analyze a question to determine a set of categories, acategory corresponding to a type of possible answers responsive to thequestion; program instructions to extract from social media data a setof opinions, wherein each opinion is from a corresponding responder on asocial media platform to which the question is sent; programinstructions to categorize an opinion from the set of opinions into acategory from the set of categories; program instructions to compute astrength of the opinion using a subset of a set of strength parameters;program instructions to rank the opinion according to the strength ofthe opinion; and program instructions to present the opinion, thestrength, and the category in a filterable presentation.
 21. Thecomputer usable program product of claim 17, further comprising:computing a responder-specific language model corresponding to aresponder of the opinion; computing a value of a responder-specificlanguage strength parameter for the opinion, wherein the strength of theopinion uses the value of the responder-specific language strengthparameter for the opinion.
 22. The computer usable program product ofclaim 21, further comprising: analyzing historical social data of theresponder to construct the responder-specific language model, whereinthe responder-specific language model establishes a threshold degree ofsentiment used by the responder in the historical social data, andwherein the responder-specific language strength parameter is indicativeof a degree of sentiment of the opinion relative to the threshold degreeof sentiment used by the responder.
 23. The computer usable programproduct of claim 17, further comprising: computing a responder-specificcultural model corresponding to a responder of the opinion; computing avalue of a responder-specific cultural strength parameter for theopinion, wherein the strength of the opinion uses the value of theresponder-specific cultural strength parameter for the opinion.
 24. Thecomputer usable program product of claim 23, further comprising:analyzing historical social data of the responder to construct theresponder-specific cultural model, wherein the responder-specificcultural model establishes a culturally-specific manner of expressingsentiments used by the responder in the historical social data, andwherein the responder-specific cultural strength parameter is indicativeof a degree of sentiment of the opinion relative to theculturally-specific manner of expressing sentiments used by theresponder.
 25. The computer usable program product of claim 17, furthercomprising: analyzing historical social data of the responder todetermine whether the responder has a personal experience supporting theopinion; and computing a value of an evidentiary support strengthparameter for the opinion according to a degree of support provided tothe opinion by the personal experience of the responder.
 26. Thecomputer usable program product of claim 17, further comprising:analyzing a result set obtained from an internet search engineresponsive to a query, the query corresponding to the question;determining from analyzing the result set whether supporting informationis available from an information source to support the opinion; andcomputing a value of an evidentiary support strength parameter for theopinion according to a degree of support provided to the opinion by thesupporting information.
 27. The computer usable program product of claim17, wherein the presentation is filterable according to a strengthparameter associated with the opinion.
 28. The computer usable programproduct of claim 17, wherein the presentation is filterable according tothe category associated with the opinion.
 29. The computer usableprogram product of claim 17, wherein the presentation is filterableaccording to a cultural strength parameter associated with the opinion.30. The computer usable program product of claim 17, wherein the rankingranks the opinion in the set of opinions.
 31. The computer usableprogram product of claim 17, wherein the ranking ranks the opinion in asubset of opinions that are in the category.
 32. The computer usableprogram product of claim 17, further comprising: computing a categorystrength using the strengths associated with each opinion in thecategory, and wherein the category is positioned in the presentationaccording to the category strength.
 33. The computer usable programproduct of claim 17, further comprising: receiving an expression of aneed for social information; transforming the expression into thequestion; and submitting the question to the social media platform. 34.The computer usable program product of claim 33, transforming theexpression into a search engine query for an internet search engine. 35.The computer usable program product of claim 17, further comprising:analyzing the question, using Natural Language Processing (NLP), toidentify a topic of the question; and analyzing historical data from adata source to determine a set of types of answers that are possible inresponse to the topic, wherein the type is in the set of types.